Evaluation of Phased Array Architectures for Weather Observations with Space-Time Processing

Abstract

Conference Title: 2024 IEEE Radar Conference (RadarConf24)

Conference Start Date: 2024, May 6

Conference End Date: 2024, May 10

Conference Location: Denver, CO, USA

Phased Array Radar (PAR) technology is showing potential to enhance atmospheric observations through its unique capabilities. Among these capabilities, space-time processing is a new way to approach improving weather observation by mitigating clutter in both Doppler and spatial domains through the joint designing of a filter response in both domains. The implementation of space-time processing can take the form of non-adaptive or adaptive space-time weights. Space-time adaptive processing (STAP) can improve clutter mitigation and detection by adaptively adjusting space-time weights. Conversely, non-adaptive space-time processing (STP) has a fixed space-time weights, which leads to sidelobe contamination, limited resolution, and potential inaccuracies in the identification of weather phenomena. This study focuses on the application of STAP and STP to simulated weather data in clutter environment to assess their respective performances. It is worth noting that while STAP offers adaptability, it comes at the cost of increased computational complexity due to its data-dependent nature. To address this challenge, one proposed solution involves the implementation of a subarray architecture within the PAR system. This approach reduces the volume of data to be processed by employing fewer receiving channels while maintaining reasonable data quality. Subsequently, this work explores the trade-offs associated with subarray systems, particularly grating lobes. A potential solution to this issue involves the implementation of an optimized transmit beam pattern. The optimized transmit beam pattern is introduced to reduce the two-way beam patterns especially at the locations of grating lobes, thereby mitigating associated challenges and optimizing system performance.